Information concerning various types of neural networks can be found, for example in the article by R. P. LIPPMANN "An introduction to computing with neural nets", IEEE ASSP Magazine, April 1987, pp. 4 to 22 which is incorporated herein by reference.
For implementation of some of the above processes it may be necessary to calculate distances between data represented by vectors or to calculate vector norms. This is the case, for example in order to implement given learning algorithms. It appeared advantageous to let the neural processor itself calculate such norm or distance. Such calculation could be executed even independently from its usage in any of the cited processes.
In this respect reference is made to an article "Neural Computation of arithmetic functions" by K. Y. SIV and J. BRUCK. Proc. IEEE, Vol. 78, No. 10, October 1990, pp. 1669-1675 which is incorporated herein by reference.
The article mentions the interest in the calculation of square roots by means of a neural network, but nothing is said about how the neural network is to be trained.
The following additional background material is incorporated herein by reference:
1. U.S. Pat. No. 4,994,982, which shows the structure of a prior art neuron;
2. British Pat. No. GB 2,236,608 A, which also shows the structure of a prior art neuron;
3. S. Renals et al., "Phoneme Classification Experiments Using Radial Basis Functions", Proceedings Internation Joint Conference on Neural Networks, pp. I. 461-i. 467, IEEE, Washington D.C., June 1989, which shows an application area in which the present invention would be useful.
4. M. Duranton et al., "Learning on VLSI: A General Purpose Neurochip", Philips J. Res. 45, 1-17, 1990 which shows a prior art neurochip and sicsusses fields of application.